Dynamically analyzing diagnostic operations data via machine learning techniques

ABSTRACT

Methods, systems, and computer program products for dynamically analyzing diagnostic operations data via machine learning techniques are provided herein. A computer-implemented method includes defining aspects of machine learning techniques to be performed in connection with diagnostic operation data analysis, including: defining dynamic analysis granularity selection functionality based on time constraints and the level of progress of the analysis; defining dynamic data classification identifier type selection functionality based on the time constraints and the level of progress of the analysis; and defining dynamic ordering of data classification identifiers during runtime based on data classification preferences, information pertaining to system workload, and information pertaining to requested analysis compliance parameters. The method also includes dynamically performing the analysis by applying the machine learning techniques to the diagnostic operation data, and outputting at least a portion of results of the analysis.

FIELD

The present application generally relates to information technology and,more particularly, to data management techniques.

BACKGROUND

Diagnostic operations, such as diagnostic dumps, are often carried outwhen a system faces one or more errors or failures. Additionally,diagnostic dump files generally contain an entire memory snapshot at thetime of failure, commonly being quite large in size. Also, diagnosticdumps often contain sensitive data, and are frequently required to beshared with third-party vendors for diagnostics, which poses a securityrisk related to exposure of sensitive data. Detecting and removing suchsensitive data in diagnostic dump files, however, is conventionally atime- and labor-intensive process, creating system-related bottlenecksand inefficiencies.

SUMMARY

In one embodiment of the present invention, techniques for dynamicallyanalyzing diagnostic operations data via machine learning techniques areprovided. An exemplary computer-implemented method includes processinguser inputs pertaining to diagnostic operation data analysis associatedwith a system, wherein the user inputs comprise one or more timeconstraints and one or more data classification preferences, anddefining, based at least in part on the processed user inputs, one ormore aspects of machine learning techniques to be performed inconnection with the diagnostic operation data analysis. In such amethod, defining the one or more aspects of the machine learningtechniques comprises defining dynamic analysis granularity selectionfunctionality based at least in part on the one or more time constraintsand the level of progress of the diagnostic operation data analysis,defining dynamic data classification identifier type selectionfunctionality based at least in part on the one or more time constraintsand the level of progress of the diagnostic operation data analysis; anddefining dynamic ordering of data classification identifiers duringruntime based at least in part on the one or more data classificationpreferences, information pertaining to system workload, and informationpertaining to one or more requested analysis compliance parameters.Further, such a method also includes dynamically performing thediagnostic operation data analysis by applying the machine learningtechniques to the diagnostic operation data, and outputting at least aportion of results of the diagnostic operation data analysis to at leastone user.

Another embodiment of the invention or elements thereof can beimplemented in the form of a computer program product tangibly embodyingcomputer readable instructions which, when implemented, cause a computerto carry out a plurality of method steps, as described herein.Furthermore, another embodiment of the invention or elements thereof canbe implemented in the form of a system including a memory and at leastone processor that is coupled to the memory and configured to performnoted method steps. Yet further, another embodiment of the invention orelements thereof can be implemented in the form of means for carryingout the method steps described herein, or elements thereof; the meanscan include hardware module(s) or a combination of hardware and softwaremodules, wherein the software modules are stored in a tangiblecomputer-readable storage medium (or multiple such media).

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating system architecture, according to anexemplary embodiment of the invention;

FIG. 2 is a flow diagram illustrating techniques according to anembodiment of the invention;

FIG. 3 is a system diagram of an exemplary computer system on which atleast one embodiment of the invention can be implemented;

FIG. 4 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 5 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

As described herein, an embodiment of the present invention includesimproving the reaction time for analysis of diagnostics system dumps andefficiently analyzing diagnostics system dumps to self-tune the extentof sensitivity analysis performed to maximize accuracy based on one ormore user constraints. At least one embodiment includes utilizing userinputs to define machine language analytics for performing dynamicgranularity-selection analysis of diagnostic system dumps for improvinganalysis reactions and/or performance time.

Such an embodiment includes performing time-based graceful degradationand possible upgradation at different intervals of time based on usertime constraints and progress of analysis. For example, with respect toimplementing a fine-grained analysis (such as, for instance, a “verbose”analysis), at least one embodiment includes processing every token in apage and determining whether to redact one or more portions. Withrespect to implementing a coarse-grained analysis (such as, for example,a page-level analysis), at least one embodiment includes merelydetermining the first sensitive token in a page and redacting the entirepage before moving on to the next page.

By way of further example, assume that the initial selected granularityof analysis is set to “verbose.” At least one embodiment can includedynamically changing the selected granularity of analysis to “concise”or “Boolean” granularity if the estimated time needed to process theremaining dump (in “verbose”) is more than the user-provided limit.

Additionally, one or more embodiments include automatically switchingamong levels of identifiers used (for example, from more preciseidentifiers to more relaxed approximate identifiers) at differentintervals of time based on user time constraints and progress of theanalysis. For example, the system can initially start processing with a“precise” identifier set, but then switch to “approximate” identifiersif the time needed to process the remaining dump is more than the userprovided limit. Further, such an embodiment can include performingdynamic analysis mode (passive mode versus active mode, for example)switching to facilitate quick(er) identification of quasi-identifiers,as well as utilizing intra-dump knowledge to effectively record thelocality of reference information about the location of actual datawithin dump files, and to effectively record the locality of hotidentifiers (that is, identifiers that are likely to appear in anapplication context) set by intra-dump knowledge.

Also, at least one embodiment includes dynamically ordering identifiersbased on user preferences, system workloads and a level of compliancerequested to persist inter-dump processing information and applicationmetadata to optimize future analysis of dump files. In one or moreembodiments, process performance is related to the size of theidentifier set. For example, if it is known that for a givenapplication, certain types of identifiers would not appear, at least oneembodiment includes tailoring the identifier set to improve search time.Additionally, one or more embodiments include searching a new diagnosticdump based on knowledge learned from a previous diagnostic dump. Forinstance, certain pages can be skipped if a previous determination wasmade that such pages do not contain sensitive data.

FIG. 1 is a diagram illustrating system architecture, according to anembodiment of the invention. By way of illustration, FIG. 1 depicts aninput file 102, which can include diagnostic dumps, logs, etc. The inputfile 102 is provided to and/or obtained by an input parser 108 (which ispart of analysis flow 106, which is carried out by smart analysis system104), which parses the input file 102 to recognize portions of parseddata 110 derived from input file 102.

As illustrated, analysis flow 106 also includes a data classifier 112,which classifies different parts of the parsed data 110 into variousclasses 114 using one or more techniques and/or models. FIG. 1 alsodepicts a sensitive data identifier 116, which determines whether theclass assignment 114 contains sensitive data. In one or moreembodiments, the sensitive data identifier 116 uses aclass-to-sensitivity mapping to analyze the classified data 114. Also,in at least one embodiment, the sensitive data identifier 116 implementsdirect identifiers and quasi-identifiers. As used herein,quasi-identifiers indicate situations wherein multiple classes should bepresent together, and in such embodiments, the nearness of data shouldbe taken into consideration. The output of sensitive data identifier 116can include data (from the classified data 114) tagged as sensitive ornon-sensitive.

Additionally, analysis flow 106 further includes a data redactioncomponent 118, which redacts the identified sensitive data from theinput file 102 and generates an output file 152. In one or moreembodiments, various user-configurable methods can be implemented by thedata redaction component 118, including, e.g., hashing, replacement, andencryption.

As also illustrated, FIG. 1 additionally depicts a dynamic identifierexecution plan 144, which is provided to and/or incorporated into theanalysis flow 106. The dynamic identifier execution plan 144 isdetermined and/or generated via the implementation of components 122,128, 134, 140 and 142, as detailed below.

User inputs 120 are provided to the smart analysis system to be used inconnection with generating the dynamic identifier execution plan 144.For example, in one or more embodiments, diagnostic dump analysis andremoval of sensitive data are important pre-processing steps, andproblem identification can only start after such pre-processing hasoccurred. In such an embodiment, one or more users can provide inputs120 to specify various constraints in order to facilitate the analysis.Such inputs can include, for example, time limit information, whereinthe user can specify the maximum permitted time to analyze the dumpfile. Other inputs 120 can include system workload information, whereinthe user can provide a list of applications which were running at thetime of a system failure, which can help to identify the size ofapplication headers and the position of data in different memory pages.Also, inputs 120 can include target application information, which canhelp to efficiently organize the selected identifiers, as well ascompliance information, wherein the user can provide a list ofcompliance parameters that the processed dump should satisfy beforereaching a third party.

As also depicted in FIG. 1, the smart analysis system 104 includes adynamic granularity selection component 122, which generates time-basedgraceful degradation information 124 as well as approximate identifiers126. With respect to time-based graceful degradation 124, given a timelimit for execution, one or more embodiments can include intelligentlyincreasing or decreasing the granularity of machine learning testing. Byway merely of example, the following granularity of processing can besupported by the system in an example embodiment: Verbose processing,wherein sensitive data identification is carried out across pageboundaries; Concise processing, wherein all of the sensitive tokens areidentified in each page individually; and Boolean processing, whereinthe identification for a page is exited after detecting a firstsensitive token.

Also, by way of example, consider a scenario wherein the diagnostic dumpprocessing has to finish in a given time limit (say, five hours). Insuch an example embodiment, initially, a normal level of processing canbe carried out, consumed time can be periodically measured, the timeneeded to complete the analysis can be periodically estimated, and ifthe time needed to complete is greater than the remaining portion of theoverall time limit, a switch to a faster level of processing can bemade. If the time needed to complete is less than the remaining portionof the overall time limit, a switch to slower more detailed processingcan be made.

With respect to approximate identifiers 126, one or more embodiments caninclude transitioning from multiple dictionaries to a single dictionary(which reduces the accuracy but facilitates faster execution). Forexample, with an approximate identifier, both “first name” and “lastname” identifiers can be merged to form a single (same) identifier.

As also depicted in FIG. 1, the smart analysis system 104 includes adynamic mode switching component 128, which generates and/or enables anactive analysis mode 130 and a passive analysis mode 132. With respectto an active mode 130, each token can be tested against all identifiers.Also, in one or more embodiments, an active mode can be designated asthe default mode of execution. With respect to a passive mode 132, eachidentifier can be executed for all tokens one by one. In an exampleembodiment, a first identifier is selected and run for all tokens, andthen a second identifier is fetched, etc. Additionally, in one or moreembodiments, with respect to quasi-identifiers, the second identifier(of a quasi-identifier) is run only if the first identifier is foundsomewhere in the record.

Additionally, the smart analysis system 104 includes an intra-dumpknowledge transfer component 134, which determines locality of referenceinformation 136, which can help to prioritize identifiers. For example,the identifiers which have a positive match in a current page will havemore priority in the subsequent pages. Also, the intra-dump knowledgetransfer component 134 determines patterns 138 of entity types. By wayof example, if a record has <first name>, <last name>, <ssn>, and <dob>for multiple identifiers, then the system can group these identifiersand prioritize their execution. Once a pattern is identified, the sameidentifiers can be run in the same order.

One or more embodiments include identifier management tools including adynamic identifier ordering component 140 and a parallel identifierexecution component 142. With respect to the dynamic identifier orderingcomponent 140, various different types of identifiers (derived fromdictionaries, regular expressions, and machine learning techniques) canbe run in series until a match is found. At least one embodimentincludes keeping track of identifiers which match, and running thoseidentifiers before the identifiers that are not matching (while thestatistics can be maintained for a given temporal window). Also,identifiers can be ordered based on application metadata and complianceparameters requested. With respect to the parallel identifier executioncomponent 142, disjoint identifiers for different compliance can beexecuted in parallel.

As depicted in FIG. 1, the outputs from components 122, 128, 134, 140and 142 are provided to and/or incorporated into the dynamic identifierexecution plan 144. For example, one or more embodiments includeordering the identifiers to be searched to improve performance. Such atype of action is similar, for example, to a database query executionplan, which might include knowing and/or estimating the likely cost ofeach search step and globally optimizing the entire search.

FIG. 1 also depicts a component directed to persisting metadata 146,which includes an inter-dump metadata component 148 and an applicationmetadata component 150. With respect to the inter-dump metadatacomponent 148, from multiple diagnostic dumps belonging to the sameapplication, at least one embodiment can include finding the part ofdump most likely to contain sensitive data. In the future runs, such anembodiment includes first analyzing the part of the diagnostic dumpwhich is most likely to contain the sensitive data. With respect to theapplication metadata component 150, patterns identified with respect toa specific application can be utilized in future runs to improveperformance, and application-specific tag information can be identifiedto help in fetching data quickly from diagnostic dump pages. Outputsfrom component 146 are provided to the data classifier 112.

Further, one or more embodiments include extending one or more of thetechniques and/or components detailed herein to distributed systems.Such an embodiment can include processing input files (e.g., diagnosticdump files) generated in a distributed system, and the analysis can behandled via combining partial dumps and/or processing individual dumps.

FIG. 2 is a flow diagram illustrating techniques according to anembodiment of the present invention. Step 202 includes processing userinputs pertaining to diagnostic operation data analysis associated witha system, wherein the user inputs comprise (i) one or more timeconstraints and (ii) one or more data classification preferences.

Step 204 includes defining, based at least in part on the processed userinputs, one or more aspects of machine learning techniques to beperformed in connection with the diagnostic operation data analysis. Inat least one embodiment, defining the one or more aspects of the machinelearning techniques includes defining dynamic analysis granularityselection functionality based at least in part on the one or more timeconstraints and the level of progress of the diagnostic operation dataanalysis, defining dynamic data classification identifier type selectionfunctionality based at least in part on the one or more time constraintsand the level of progress of the diagnostic operation data analysis, anddefining dynamic ordering of data classification identifiers duringruntime based at least in part on the one or more data classificationpreferences, information pertaining to system workload, and informationpertaining to one or more requested analysis compliance parameters.

Further, analysis granularity selection functionality can includeselecting between (i) verbose analysis granularity, (ii) conciseanalysis granularity, and (iii) Boolean analysis granularity. Also,dynamic data classification identifier type selection functionality caninclude selecting between (i) one or more precise data classificationidentifiers and (ii) one or more approximate data classificationidentifiers.

Step 206 includes dynamically performing the diagnostic operation dataanalysis by applying the machine learning techniques to the diagnosticoperation data. Applying the class-to-sensitivity mapping technique caninclude implementing (i) one or more direct sensitivity identifiers and(ii) one or more quasi sensitivity identifiers. In one or moreembodiments, quasi sensitivity identifiers (i) require two or moreparticular classes being present together and (ii) consider nearness ofdata. Additionally, in at least one embodiment, defining aspects of themachine learning techniques includes defining dynamic analysis modeswitching functionality based at least in part on a lack ofidentification of one or more particular data classification identifiersin the diagnostic operation data analysis. In such an embodiment,analysis mode switching functionality includes selecting between (i) anactive mode of analysis and (ii) a passive mode of analysis, wherein theactive mode of analysis facilitates identification of one or morequasi-identifiers in the diagnostic operation data analysis.

Step 208 includes outputting at least a portion of results of thediagnostic operation data analysis to at least one user.

The techniques depicted in FIG. 2 can also include persisting diagnosticoperation data processing information and related application metadata,and optimizing one or more subsequent diagnostic operation data analysesbased at least in part on the diagnostic operation data processinginformation and the related application metadata. Further, at least oneembodiment includes utilizing intra-diagnostic operation data knowledgeto record the locality of reference information pertaining to one ormore locations of particular data within the diagnostic operation data,as well as utilizing intra-diagnostic operation data knowledge to recordlocality of one or more identifiers.

Also, in one or more embodiments, the system includes a distributedsystem. Such an embodiment can include self-tuning a level ofsensitivity for the diagnostic operation data generated in thedistributed system.

The techniques depicted in FIG. 2 can also, as described herein, includeproviding a system, wherein the system includes distinct softwaremodules, each of the distinct software modules being embodied on atangible computer-readable recordable storage medium. All of the modules(or any subset thereof) can be on the same medium, or each can be on adifferent medium, for example. The modules can include any or all of thecomponents shown in the figures and/or described herein. In anembodiment of the invention, the modules can run, for example, on ahardware processor. The method steps can then be carried out using thedistinct software modules of the system, as described above, executingon a hardware processor. Further, a computer program product can includea tangible computer-readable recordable storage medium with code adaptedto be executed to carry out at least one method step described herein,including the provision of the system with the distinct softwaremodules.

Additionally, the techniques depicted in FIG. 2 can be implemented via acomputer program product that can include computer useable program codethat is stored in a computer readable storage medium in a dataprocessing system, and wherein the computer useable program code wasdownloaded over a network from a remote data processing system. Also, inan embodiment of the invention, the computer program product can includecomputer useable program code that is stored in a computer readablestorage medium in a server data processing system, and wherein thecomputer useable program code is downloaded over a network to a remotedata processing system for use in a computer readable storage mediumwith the remote system.

An embodiment of the invention or elements thereof can be implemented inthe form of an apparatus including a memory and at least one processorthat is coupled to the memory and configured to perform exemplary methodsteps.

Additionally, an embodiment of the present invention can make use ofsoftware running on a computer or workstation. With reference to FIG. 3,such an implementation might employ, for example, a processor 302, amemory 304, and an input/output interface formed, for example, by adisplay 306 and a keyboard 308. The term “processor” as used herein isintended to include any processing device, such as, for example, onethat includes a CPU (central processing unit) and/or other forms ofprocessing circuitry. Further, the term “processor” may refer to morethan one individual processor. The term “memory” is intended to includememory associated with a processor or CPU, such as, for example, RAM(random access memory), ROM (read only memory), a fixed memory device(for example, hard drive), a removable memory device (for example,diskette), a flash memory and the like. In addition, the phrase“input/output interface” as used herein, is intended to include, forexample, a mechanism for inputting data to the processing unit (forexample, mouse), and a mechanism for providing results associated withthe processing unit (for example, printer). The processor 302, memory304, and input/output interface such as display 306 and keyboard 308 canbe interconnected, for example, via bus 310 as part of a data processingunit 312. Suitable interconnections, for example via bus 310, can alsobe provided to a network interface 314, such as a network card, whichcan be provided to interface with a computer network, and to a mediainterface 316, such as a diskette or CD-ROM drive, which can be providedto interface with media 318.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in associated memory devices (for example, ROM, fixed orremovable memory) and, when ready to be utilized, loaded in part or inwhole (for example, into RAM) and implemented by a CPU. Such softwarecould include, but is not limited to, firmware, resident software,microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 302 coupled directly orindirectly to memory elements 304 through a system bus 310. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including, but not limited to, keyboards308, displays 306, pointing devices, and the like) can be coupled to thesystem either directly (such as via bus 310) or through intervening I/Ocontrollers (omitted for clarity).

Network adapters such as network interface 314 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modems andEthernet cards are just a few of the currently available types ofnetwork adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 312 as shown in FIG. 3)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the components detailed herein. Themethod steps can then be carried out using the distinct software modulesand/or sub-modules of the system, as described above, executing on ahardware processor 302. Further, a computer program product can includea computer-readable storage medium with code adapted to be implementedto carry out at least one method step described herein, including theprovision of the system with the distinct software modules.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations thereof, for example, application specific integratedcircuit(s) (ASICS), functional circuitry, an appropriately programmeddigital computer with associated memory, and the like. Given theteachings of the invention provided herein, one of ordinary skill in therelated art will be able to contemplate other implementations of thecomponents of the invention.

Additionally, it is understood in advance that implementation of theteachings recited herein are not limited to a particular computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any type of computing environmentnow known or later developed.

For example, cloud computing is a model of service delivery for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (for example, networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service. Thiscloud model may include at least five characteristics, at least threeservice models, and at least four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (for example, country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (for example, storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (for example, web-basede-mail). The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (for example, host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(for example, mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (for example, cloud burstingfor load-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 4 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 4) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75. In one example, management layer 80 may provide thefunctions described below. Resource provisioning 81 provides dynamicprocurement of computing resources and other resources that are utilizedto perform tasks within the cloud computing environment. Metering andPricing 82 provide cost tracking as resources are utilized within thecloud computing environment, and billing or invoicing for consumption ofthese resources.

In one example, these resources may include application softwarelicenses. Security provides identity verification for cloud consumersand tasks, as well as protection for data and other resources. Userportal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and dynamic diagnostic operation dataanalysis 96, in accordance with the one or more embodiments of thepresent invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of anotherfeature, step, operation, element, component, and/or group thereof.

At least one embodiment of the present invention may provide abeneficial effect such as, for example, performing time-baseddegradation with possible upgradation at different intervals of timebased on user time constraints and progress of analysis, utilizingintra-diagnostic dump knowledge to record locality of referenceinformation pertaining to the location of data within dump files andlocality of identifiers set in accordance with the intra-diagnostic dumpknowledge.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:processing user inputs pertaining to diagnostic operation data analysisassociated with a system, wherein the user inputs comprise (i) one ormore time constraints and (ii) one or more data classificationpreferences; defining, based at least in part on the processed userinputs, one or more aspects of machine learning techniques to beperformed in connection with the diagnostic operation data analysis,wherein said defining the one or more aspects of the machine learningtechniques comprises: defining dynamic analysis granularity selectionfunctionality based at least in part on (i) the one or more timeconstraints and (ii) the level of progress of the diagnostic operationdata analysis; defining dynamic data classification identifier typeselection functionality based at least in part on (i) the one or moretime constraints and (ii) the level of progress of the diagnosticoperation data analysis; and defining dynamic ordering of dataclassification identifiers during runtime based at least in part on (i)the one or more data classification preferences, (ii) informationpertaining to system workload, and (iii) information pertaining to oneor more requested analysis compliance parameters; dynamically performingthe diagnostic operation data analysis by applying the machine learningtechniques to the diagnostic operation data; outputting at least aportion of results of the diagnostic operation data analysis to at leastone user; and automatically tuning at least a portion of the machinelearning techniques based at least in part on the results of thediagnostic operation data analysis; wherein the method is carried out byat least one computing device.
 2. The computer-implemented method ofclaim 1, wherein analysis granularity selection functionality comprisesselecting between (i) verbose analysis granularity, (ii) conciseanalysis granularity, and (iii) Boolean analysis granularity.
 3. Thecomputer-implemented method of claim 1, wherein dynamic dataclassification identifier type selection functionality comprisesselecting between (i) one or more precise data classificationidentifiers and (ii) one or more approximate data classificationidentifiers.
 4. The computer-implemented method of claim 1, comprising:persisting (i) diagnostic operation data processing information and (ii)related application metadata; and optimizing one or more subsequentdiagnostic operation data analyses based at least in part on (i) thediagnostic operation data processing information and (ii) the relatedapplication metadata.
 5. The computer-implemented method of claim 1,comprises: utilizing intra-diagnostic operation data knowledge to recordlocality of reference information pertaining to one or more locations ofparticular data within the diagnostic operation data.
 6. Thecomputer-implemented method of claim 1, comprises: utilizingintra-diagnostic operation data knowledge to record locality of one ormore identifiers.
 7. The computer-implemented method of claim 1, whereinthe system comprises a distributed system.
 8. The computer-implementedmethod of claim 7, comprises: self-tuning a level of sensitivity for thediagnostic operation data generated in the distributed system.
 9. Thecomputer-implemented method of claim 8, wherein self-tuning comprisingapplying the class-to-sensitivity mapping technique comprisesimplementing (i) one or more direct sensitivity identifiers and (ii) oneor more quasi sensitivity identifiers.
 10. The computer-implementedmethod of claim 9, wherein the one or more quasi sensitivity identifiers(i) require two or more particular classes being present together and(ii) consider nearness of data.
 11. The computer-implemented method ofclaim 9, wherein said defining the one or more aspects of the machinelearning techniques comprises defining dynamic analysis mode switchingfunctionality based at least in part on a lack of identification of oneor more particular data classification identifiers in the diagnosticoperation data analysis.
 12. The computer-implemented method of claim11, wherein analysis mode switching functionality comprises selectingbetween (i) an active mode of analysis and (ii) a passive mode ofanalysis, wherein the active mode of analysis facilitates identificationof one or more quasi-identifiers in the diagnostic operation dataanalysis.
 13. A computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a computing device to cause thecomputing device to: process user inputs pertaining to diagnosticoperation data analysis associated with a system, wherein the userinputs comprise (i) one or more time constraints and (ii) one or moredata classification preferences; define, based at least in part on theprocessed user inputs, one or more aspects of machine learningtechniques to be performed in connection with the diagnostic operationdata analysis, wherein said defining the one or more aspects of themachine learning techniques comprises: defining dynamic analysisgranularity selection functionality based at least in part on (i) theone or more time constraints and (ii) the level of progress of thediagnostic operation data analysis; defining dynamic data classificationidentifier type selection functionality based at least in part on (i)the one or more time constraints and (ii) the level of progress of thediagnostic operation data analysis; and defining dynamic ordering ofdata classification identifiers during runtime based at least in part on(i) the one or more data classification preferences, (ii) informationpertaining to system workload, and (iii) information pertaining to oneor more requested analysis compliance parameters; dynamically performthe diagnostic operation data analysis by applying the machine learningtechniques to the diagnostic operation data; output at least a portionof results of the diagnostic operation data analysis to at least oneuser; and automatically tune at least a portion of the machine learningtechniques based at least in part on the results of the diagnosticoperation data analysis.
 14. The computer program product of claim 13,wherein analysis granularity selection functionality comprises selectingbetween (i) verbose analysis granularity, (ii) concise analysisgranularity, and (iii) Boolean analysis granularity.
 15. The computerprogram product of claim 13, wherein dynamic data classificationidentifier type selection functionality comprises selecting between (i)one or more precise data classification identifiers and (ii) one or moreapproximate data classification identifiers.
 16. The computer programproduct of claim 13, wherein the program instructions executable by acomputing device further cause the computing device to: utilizeintra-diagnostic operation data knowledge to record locality ofreference information pertaining to one or more locations of particulardata within the diagnostic operation data; and utilize intra-diagnosticoperation data knowledge to record locality of one or more identifiers.17. A system comprising: a memory; and at least one processor operablycoupled to the memory and configured for: processing user inputspertaining to diagnostic operation data analysis associated with asystem, wherein the user inputs comprise (i) one or more timeconstraints and (ii) one or more data classification preferences;defining, based at least in part on the processed user inputs, one ormore aspects of machine learning techniques to be performed inconnection with the diagnostic operation data analysis, wherein saiddefining the one or more aspects of the machine learning techniquescomprises: defining dynamic analysis granularity selection functionalitybased at least in part on (i) the one or more time constraints and (ii)the level of progress of the diagnostic operation data analysis;defining dynamic data classification identifier type selectionfunctionality based at least in part on (i) the one or more timeconstraints and (ii) the level of progress of the diagnostic operationdata analysis; and defining dynamic ordering of data classificationidentifiers during runtime based at least in part on (i) the one or moredata classification preferences, (ii) information pertaining to systemworkload, and (iii) information pertaining to one or more requestedanalysis compliance parameters; dynamically performing the diagnosticoperation data analysis by applying the machine learning techniques tothe diagnostic operation data; outputting at least a portion of resultsof the diagnostic operation data analysis to at least one user; andautomatically tuning at least a portion of the machine learningtechniques based at least in part on the results of the diagnosticoperation data analysis.
 18. The system of claim 17, wherein analysisgranularity selection functionality comprises selecting between (i)verbose analysis granularity, (ii) concise analysis granularity, and(iii) Boolean analysis granularity.
 19. The system of claim 17, whereindynamic data classification identifier type selection functionalitycomprises selecting between (i) one or more precise data classificationidentifiers and (ii) one or more approximate data classificationidentifiers.
 20. The system of claim 17, wherein the at least oneprocessor is further configured for: utilizing intra-diagnosticoperation data knowledge to record locality of reference informationpertaining to one or more locations of particular data within thediagnostic operation data; and utilizing intra-diagnostic operation dataknowledge to record locality of one or more identifiers.